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Consistent Wiener filtering for audio source separation

Jonathan Le Roux 1 Emmanuel Vincent 2
2 METISS - Speech and sound data modeling and processing
IRISA - Institut de Recherche en Informatique et Systèmes Aléatoires, Inria Rennes – Bretagne Atlantique
Abstract : Wiener filtering is one of the most ubiquitous tools in signal processing, in particular for signal denoising and source separation. In the context of audio, it is typically applied in the time-frequency domain by means of the short-time Fourier transform (STFT). Such processing does generally not take into account the relationship between STFT coefficients in different time-frequency bins due to the redundancy of the STFT, which we refer to as consistency. We propose to enforce this relationship in the design of the Wiener filter, either as a hard constraint or as a soft penalty. We derive two conjugate gradient algorithms for the computation of the filter coefficients and show improved audio source separation performance compared to the classical Wiener filter both in oracle and in blind conditions.
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Contributor : Emmanuel Vincent Connect in order to contact the contributor
Submitted on : Tuesday, October 16, 2012 - 11:40:52 PM
Last modification on : Wednesday, June 16, 2021 - 3:42:15 AM
Long-term archiving on: : Thursday, January 17, 2013 - 11:55:37 AM


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  • HAL Id : hal-00725350, version 2


Jonathan Le Roux, Emmanuel Vincent. Consistent Wiener filtering for audio source separation. [Research Report] RR-8049, 2012. ⟨hal-00725350v2⟩



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